We perform one-shot classification
for 20-way Omniglot within alphabet task.
Images are resized to \(64 \times 64\)  and passed through a four-layer convolutional network commonly used in few-shot models [1]}, [2]}. We use kernel of size 3, with padding and the stride of 1, and we use max-pooling only after the first two convolutional layers.
This results in a \(16 \times 16\)  feature map with \(d_f=128\)  channels which we flatten into a sequence of 256 local features of size \(d_f\)  that we use as input tokens to the Transformer layers. We use simple 1D positional encoding [3]} for the inputs to the Transformer block and we use \(K=2048\)  dimensions in the aggregation network in (REF ).
